1. Field of the Invention
The present invention relates to computer-assisted diagnosis (CADx) and, in particular, to an interactive computer-aided diagnosis (ICAD) method and system for assisting diagnosis of lung nodules in digital volumetric medical images.
2. Discussion of Prior Art
In Computed Tomography (CT) lung cancer screening and diagnosis, pulmonary nodule segmentation is needed for analysis, such as quantification, classification, and three-dimensional (3D) rendering, and therefore needed to be accurate. However, due to the complex connectivity of pulmonary nodules with surrounding anatomical structures, fully automatic segmentation methods cannot always provide desirable segmentation results. For example, when a pulmonary nodule is attached to a patient's chest wall or vessels, a segmentation results generated by fully automatic method may produce undesirable results.
According to existing proposals, segmentation correction can be implemented by manually drawing polygons on 2D slices to indicate the segmentation results. Another proposed approach, 3D editing, provides tools for manually cutting and pasting arbitrarily on the target object in 3D.
Manually drawing polygons on 2D slices has several drawbacks. In many of the cases, there is no ground truth of nodule segmentation, and therefore manual correction does not necessarily provide better results. In addition, it can be hard to edit 3D data on 2D slices. Further, manual correction can be difficult to reproduce, and therefore, can result in inconsistencies in follow-up studies. Consistency is an important characteristic for nodule segmentation in the sequential CT lung studies since the growth rate or the size change of a nodule is one of the most important measurements for the lung cancer diagnosis.
The 3D editing approach also has a problem of inconsistency. Since the nodule in most cases is in the size of millimeters, even a small handshaking would make the reproducibility too low to be accepted clinically.
Therefore, a need exists for a method of interactive segmentation that can extract nodules in desirable and substantially instantaneously manner is needed.